In the prior art, many attempts at configuring and optimizing Enterprise Server systems for different types of customers involved considerable guesswork and trial-by-error methods in trying to determine the best configuration of Servers and Server Farms which would be most suitable for the customer.
As was previously discussed in the co-pending applications, many times a particular customer is not exactly sure what his present and what his future requirements are or will be, and he must be led through a series of interviews and communication processes to establish parameters for his intended network operations. From this, there is developed what is called a “Customer Profile”.
It is most desirable to develop some orderly arrangement to collect and manage the information from the customer's profile in order to configure a particular type of Server Farm system which will be useful to provide the optimum delivery of services to the customer and with a minimum amount of cost, price and downtime.
When there are many large groups of users involved, then there is often used what are called “Metafarms”, that is to say, groups of servers which form a Farm and then are connected in clusters to form a series of Server Farms. The present method involves an algorithm which will take a rather precise estimate of a customer's profile and then provide a set of configuration recommendations for Server Farms which will subdivide the customer's users into manageable-size Server Farms. Later, this information can be applied to the Thin Client Sizing Tool general algorithm in order to further enhance the solutions to be presented to a potential customer. The present method provides solutions generated which are based on benchmark numbers and certain formulae determined by design engineers to be the most efficient.
Thus, the presently-disclosed method takes into account server preferences and the server's subsequent benchmark testing information in order to design an optimum configuration which will be most suitable to the customer or user. Recommendations are made to subdivide and allocate large groups of Users into Server Farms which are clustered into a Metafarm having an optimized configuration for the particular enterprise.